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Found 7 papers, 1 papers with code
MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory
To address this challenge, we propose a novel framework for semantic segmentation of a temporal sequence of LiDAR point clouds that utilizes a memory network to store, update and retrieve past information.
4D-Former: Multimodal 4D Panoptic Segmentation
4D panoptic segmentation is a challenging but practically useful task that requires every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and individual objects to be segmented and tracked over time.
Ranked #1 on
Panoptic Tracking
on Panoptic nuScenes val
MoSS: Monocular Shape Sensing for Continuum Robots
A two-segment tendon-driven continuum robot is used for data collection and testing, demonstrating accurate (mean shape error of 0. 91 mm, or 0. 36% of robot length) and real-time (70 fps) shape sensing on real-world data.
CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds
A fast and accurate panoptic segmentation system for LiDAR point clouds is crucial for autonomous driving vehicles to understand the surrounding objects and scenes.
SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering
Thus, we propose to use a novel centroid-aware repel loss as an additional term to effectively supervise the network to differentiate each object cluster with its neighbours.
GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network
GP-S3Net is a proposal-free approach in which no object proposals are needed to identify the objects in contrast to conventional two-stage panoptic systems, where a detection network is incorporated for capturing instance information.
(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network
Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority.
Ranked #3 on
3D Semantic Segmentation
on nuScenes
